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LLMs improve social deduction skills via reinforcement fine-tuning

By PulseAugur Editorial · [1 sources] ·

Researchers have explored how reinforcement fine-tuning (RFT) impacts the social behavior of large language models (LLMs) by having them play social deduction games. The study found that while direct optimization of win-loss outcomes did not reliably improve social deduction abilities, RFT significantly enhanced social reading skills, such as inferring hidden roles and predicting other agents' decisions. Furthermore, RFT improved social influence, including vote steering and collective decision-making, though these gains were more dependent on specific rewards and interaction settings. The research also demonstrated that multi-agent social-cognitive reinforcement fine-tuning, which combines social reading and influence signals, further boosted LLMs' performance in these games and led to more favorable human evaluations of their strategic competence and social usefulness. AI

IMPACT This research could lead to LLMs that are more adept at complex social interactions and strategic decision-making in multi-agent environments.

RANK_REASON The cluster contains an academic paper detailing a new method for training LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs improve social deduction skills via reinforcement fine-tuning

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The cluster contains an academic paper detailing a new method for training LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Ying Li ·

    Playing social deduction games with reinforcement fine-tuned large language models

    Reinforcement fine-tuning (RFT) is increasingly used in applications where large language models (LLMs) interact with humans and other agents. Here we use social deduction games to study how RFT changes LLMs' social behaviour. We let fine-tuned and base LLM agents play hidden-rol…

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